<HashMap><database>biostudies-literature</database><scores/><additional><omics_type>Unknown</omics_type><volume>74</volume><submitter>Soda P</submitter><funding>Amazon Web Services</funding><pubmed_abstract>Recent epidemiological data report that worldwide more than 53 million people have been infected by SARS-CoV-2, resulting in 1.3 million deaths. The disease has been spreading very rapidly and few months after the identification of the first infected, shortage of hospital resources quickly became a problem. In this work we investigate whether artificial intelligence working with chest X-ray (CXR) scans and clinical data can be used as a possible tool for the early identification of patients at risk of severe outcome, like intensive care or death. Indeed, further to induce lower radiation dose than computed tomography (CT), CXR is a simpler and faster radiological technique, being also more widespread. In this respect, we present three approaches that use features extracted from CXR images,</pubmed_abstract><journal>Medical image analysis</journal><pagination>102216</pagination><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/S-EPMC8401374</full_dataset_link><repository>biostudies-literature</repository><pubmed_title>AIforCOVID: Predicting the clinical outcomes in patients with COVID-19 applying AI to chest-X-rays. An Italian multicentre study.</pubmed_title><pmcid>PMC8401374</pmcid><pubmed_authors>Simeone A</pubmed_authors><pubmed_authors>Soda P</pubmed_authors><pubmed_authors>Iannello G</pubmed_authors><pubmed_authors>Oliva G</pubmed_authors><pubmed_authors>Carrafiello G</pubmed_authors><pubmed_authors>Cordelli E</pubmed_authors><pubmed_authors>Cariati M</pubmed_authors><pubmed_authors>Preda L</pubmed_authors><pubmed_authors>Callea G</pubmed_authors><pubmed_authors>Valbusa G</pubmed_authors><pubmed_authors>Cozzi D</pubmed_authors><pubmed_authors>Castorani G</pubmed_authors><pubmed_authors>Stellato E</pubmed_authors><pubmed_authors>Cellina M</pubmed_authors><pubmed_authors>Sona D</pubmed_authors><pubmed_authors>Fazzini D</pubmed_authors><pubmed_authors>Tessadori J</pubmed_authors><pubmed_authors>Del Bue A</pubmed_authors><pubmed_authors>Guarrasi V</pubmed_authors><pubmed_authors>Panella S</pubmed_authors><pubmed_authors>Ali M</pubmed_authors><pubmed_authors>Akbar MU</pubmed_authors><pubmed_authors>Miele V</pubmed_authors><pubmed_authors>D'Amico NC</pubmed_authors><pubmed_authors>Papa S</pubmed_authors><pubmed_authors>Bortolotto C</pubmed_authors><pubmed_authors>Sicilia R</pubmed_authors><pubmed_authors>Tedoldi F</pubmed_authors></additional><is_claimable>false</is_claimable><name>AIforCOVID: Predicting the clinical outcomes in patients with COVID-19 applying AI to chest-X-rays. An Italian multicentre study.</name><description>Recent epidemiological data report that worldwide more than 53 million people have been infected by SARS-CoV-2, resulting in 1.3 million deaths. The disease has been spreading very rapidly and few months after the identification of the first infected, shortage of hospital resources quickly became a problem. In this work we investigate whether artificial intelligence working with chest X-ray (CXR) scans and clinical data can be used as a possible tool for the early identification of patients at risk of severe outcome, like intensive care or death. Indeed, further to induce lower radiation dose than computed tomography (CT), CXR is a simpler and faster radiological technique, being also more widespread. In this respect, we present three approaches that use features extracted from CXR images,</description><dates><release>2021-01-01T00:00:00Z</release><publication>2021 Dec</publication><modification>2026-06-03T17:53:45.005Z</modification><creation>2022-02-11T11:18:55.542Z</creation></dates><accession>S-EPMC8401374</accession><cross_references><pubmed>34492574</pubmed><doi>10.1016/j.media.2021.102216</doi></cross_references></HashMap>